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Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

What Makes GPUs, GPU Databases Ideal for BI?

What makes GPU databases ideal for BI? That’s what a new white paper from SQream DB wants to explain — incorporating real-world use cases to explain how you can turn your existing BI pipeline into “a more capable, next-generation big data analytics system.” Download the new report, courtesy of SQream DB, to learn more about how GPUs and GPU databases can help you organize and benefit from your next big data analytics system.

UC Berkeley Graduate Receives ACM Doctoral Dissertation Award

ACM, the Association for Computing Machinery, announced that Chelsea Finn receives the 2018 ACM Doctoral Dissertation Award for her dissertation, “Learning to Learn with Gradients.” In her thesis, Finn introduced algorithms for meta-learning that enable deep networks to solve new tasks from small data sets, and demonstrated how her algorithms can be applied in areas including computer vision, reinforcement learning and robotics.

Take Your Business Use Cases to the Next Level with AI & ML

Industry leader, TIBCO released insights from a survey “CXO Innovation Survey” that polled 600+ c-suite executives about the key AI/ML use cases that are being used in businesses today. According to the survey, data security is considered the top AI/ML use case among c-suite executives. Included are the top rated AI and ML use cases.

Accelerating Training for AI Deep Learning Networks with “Chunking”

At the International Conference on Learning Representations on May 6, IBM Research will share a deeper look around how chunk-based accumulation can speed the training for deep learning networks used for artificial intelligence (AI).

The insideBIGDATA IMPACT 50 List for Q2 2019

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

The AI Ethics Deficit — 94% of IT Leaders Call for More Attention to Responsible and Ethical AI Development

The results of a new study on AI ethics was released. According to research conducted by Vason Bourne on behalf of SnapLogic, studied the views and perspectives of IT decision-makers (ITDMs) across industries, asking key questions such as: who bears primary responsibility to ensure AI is developed ethically and responsibly, will global expert consortiums impact the future development of AI, and should AI be regulated and, if so, by whom?

Best of arXiv.org for AI, Machine Learning, and Deep Learning – February 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Alegion Outlines the 4 Most Prevalent Types of AI Bias

AI systems are becoming more and more of the norm as machine and deep learning gain grown — especially within the data center and colocation markets. That said, Artificial Intelligence systems are only as good as their underlying mathematics and the data they are trained on. Download a new report from Alegion to further understand the bias behind machine learning and how to avoid four potential pitfalls.

Global Artificial Intelligence Patent Survey

In this contributed article, Aaron Gin, Ph.D., partner and Margot M. Wilson, associate, with McDonnell Boehnen Hulbert & Berghoff LLP, explore AI-related patenting trends in various international jurisdictions and provides information on recent developments, common patentability issues, and tips for navigating similar trends in United States patent prosecution.